detect_protected_materialDetect copyrighted text in user input — famous lyrics, literary openings, proprietary code.
Brainiall Protected Material engine. Returns matched spans with source attribution.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to scan for copyrighted material |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description need not repeat safety. It adds value by disclosing the return behavior ('Returns matched spans with source attribution'), which is not present in the schema. The description aligns with the annotations and provides useful behavioral context beyond them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the first sentence states the core function and examples, and the second adds the return behavior. The phrase 'Brainiall Protected Material engine' is slightly redundant branding, but it does not detract significantly. Overall, every meaningful piece of information is presented efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, read-only detection tool, the description is largely complete: it says what is detected, and because there is no output schema, it properly explains the return format as matched spans with source attribution. It could mention the absence of matches behavior or length limits, but the schema covers maxLength and the description is sufficient for most agent decisions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the single parameter 'text' as 'Text to scan for copyrighted material' with 100% coverage. The description reinforces this with examples of what constitutes protected material but does not add new parameter-level information such as encoding requirements or format nuances. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource ('copyrighted text in user input') and the action ('detect'), with concrete examples that delineate it from sibling analysis tools like detect_pii and detect_prompt_injection. The specificity of 'famous lyrics, literary openings, proprietary code' makes the purpose unmistakable, and the sibling list confirms this is a distinct detection category.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case ('Detect copyrighted text in user input') but does not explicitly state when to use this tool over alternatives, nor does it give exclusion criteria such as 'for PII use detect_pii'. The examples imply the domain, but an agent would need to infer when this is the correct choice among the many sibling detection tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map cleanly to distinct capabilities, and the descriptions make the intended use clear. A few adjacent pairs—extract_entities vs link_entities_to_wikidata and detect_pii vs detect_conversational_pii—require careful selection, but they are distinguishable by their stated outputs.
Names are uniformly lowercase snake_case and mostly follow a verb_object pattern, such as analyze_*, detect_*, extract_*, summarize_text, and translate_text. A few outliers like aspect_sentiment, fraud_feedback, and knowledge_ingest break the verb-first feel, but the overall pattern remains predictable.
At 22 tools, this is on the heavy side of the borderline range. Each tool has a distinct job, but the mix of core NLP, safety, fraud, health-checking, and knowledge-base management makes the surface feel sprawling rather than tightly scoped.
The core NLP coverage is broad: sentiment, toxicity, PII, entities, QA, summarization, translation, and groundedness are all present. However, the knowledge-base tools support ingest/list/query but no delete or update, creating a dead end when documents need correction or removal.